Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20×\times Speedup

Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality

Sub-volume-based Denoising Diffusion Probabilistic Model for Cone-beam CT Reconstruction from Incomplete Data

Deep learning (DL) has emerged as a new approach in the field of computed tomography (CT) with many applicaitons. A primary example is CT reconstruction from incomplete data, such as sparse-view image reconstruction. However, applying DL to sparse-view cone-beam CT (CBCT) remains challenging. Many models learn the mapping from sparse-view CT images to the ground truth but often fail to achieve satisfactory performance. Incorporating sinogram data and performing dual-domain reconstruction improve image quality with artifact suppression, but a straightforward 3D implementation requires storing an entire 3D sinogram in memory and many parameters of dual-domain networks. This remains a major challenge, limiting further research, development and applications. In this paper, we propose a sub-volume-based 3D denoising diffusion probabilistic model (DDPM) for CBCT image reconstruction from down-sampled data. Our DDPM network, trained on data cubes extracted from paired fully sampled sinograms and down-sampled sinograms, is employed to inpaint down-sampled sinograms. Our method divides the entire sinogram into overlapping cubes and processes them in parallel on multiple GPUs, successfully overcoming the memory limitation. Experimental results demonstrate that our approach effectively suppresses few-view artifacts while preserving textural details faithfully

Is there relationship between air quality and China’s stock market? Evidence from industrial heterogeneity

This study investigates the unsymmetrical effect from air quality (AQ) to stock return (SR) in China’s different industries. Depending on quantile-on-quantile (QQ) test, it draws the important results in following aspects. For tourism, iron and steel, and automobile industries, their coefficient values between AQ and SR turn into negative from positive with deteriorating AQ. Conversely, the coefficients in the wind power, hydro power, thermal power, environmental protection, and medical equipment industries turn positive from negative. Some contributions are thus drawn when compared to existing literatures. Government industrial policy is regarded as an important supplement in explaining mechanism from AQ to SR, except investor sentiment. Industrial heterogeneity is seriously treated in this paper due to different industries have different responses to AQ. Besides, the QQ test is able to capture nexus between AQ and SR in specific quantiles through embedding non-parametric estimation into conventional quantile approach. Therefore, investors should avoid biased trading decisions under different air qualities. Meanwhile, government intervention is paid special attention when appearing serious air pollution

Cross-Edge Orchestration of Serverless Functions with Probabilistic Caching

Serverless edge computing adopts an event-based paradigm that provides back-end services on an as-used basis, resulting in efficient resource utilization. To improve the end-to-end latency and revenue, service providers need to optimize the number and placement of serverless containers while considering the system cost incurred by the provisioning. The particular reason for this circumstance is that frequently creating and destroying containers not only increases the system cost but also degrades the time responsiveness due to the cold-start process. Function caching is a common approach to mitigate the coldstart issue. However, function caching requires extra hardware resources and hence incurs extra system costs. Furthermore, the dynamic and bursty nature of serverless invocations remains an under-explored area. Hence, it is vitally important for service providers to conduct a context-aware request distribution and container caching policy for serverless edge computing. In this paper, we study the request distribution and container caching problem in serverless edge computing. We prove the proposed problem is NP-hard and hence difficult to find a global optimal solution. We jointly consider the distributed and resource constrained nature of edge computing and propose an optimized request distribution algorithm that adapts to the dynamics of serverless invocations with a theoretical performance guarantee. Also, we propose a context-aware probabilistic caching policy that incorporates a number of characteristics of serverless invocations. Via simulation and implementation results, we demonstrate the superiority of the proposed algorithm by outperforming existing caching policies in terms of the overall system cost and cold-start frequency by up to 62.1% and 69.1%, respectively

Bis(2,6-dihy­droxy­benzoato-κ2 O 1 ,O 1′)(nitrato-κ2 O,O′)bis­(1,10-phenanthroline-κ2 N,N′)gadolinium(III)

In the mononuclear title complex, [Gd(C7H5O3)2(NO3)(C12H8N2)2], the Gd atom is in a pseudo-bicapped square-anti­prismatic geometry formed by four N atoms from two chelating 1,10-phenanthroline (phen) ligands and by six O atoms, four from two 2,6-dihy­droxy­benzoate (DHB) ligands and the other two from a nitrate anion. π–π stacking inter­actions between phen–DHB [centroid–centroid distances = 3.5334 (18) and 3.8414 (16) Å] and phen–phen [face-to-face separation = 3.4307 (17) Å] ligands of adjacent complex molecules stabilize the crystal structure. Intra­molecular O—H⋯O hydrogen bonds are observed in the DHB ligands